A key challenge in climate science is to distinguish temperature changes in response to external forcing (e.g., global warming in response to anthropogenic greenhouse gasses) from temperature changes due to atmosphere-ocean internal variability. Extended integrations of forced and unforced climate models are often used for this purpose. In Wills et al. (2018), we demonstrated a novel method called low-frequency component analysis (LFCA), which separates modes of internal variability from global warming based on differences in time scales and spatial patterns, without relying on climate models.

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